Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 1212

  • County level statistics data of Tibetan Plateau (1980-2015)

    County level statistics data of Tibetan Plateau (1980-2015)

    The data set contains agricultural economic data of all counties and regions in the Tibetan Plateau in 1980-2015, and covering the total number of households and total population in rural areas, agricultural population, rural labor force, cultivated land, paddy field area, the dry land area, power of agricultural machinery, agricultural vehicles, mechanical ploughing area, irrigation area, consumption of chemical fertilizers electricity use, gross output value of agriculture, forestry, animal husbandry and fishery, the output of cattle, pig, sheep, meat, poultry, and fish, the sown area of grain, the output of grain, cotton, oil and all kinds of crops, and characteristic agricultural products and livestock production and other relevant data.The data came from the statistical yearbook of the provinces included in the Tibetan Plateau.The data are of good quality and can be used to analyze the socio-economic and agricultural development of qinghai-tibet plateau.

    2021-07-29 4475 1423

  • Cold and Arid Research Network of Lanzhou university (phenology camera observation data set of Liancheng Station, 2020)

    Cold and Arid Research Network of Lanzhou university (phenology camera observation data set of Liancheng Station, 2020)

    The data set contains the phenological camera observation data of Liancheng of the cold and arid area scientific observation network of Lanzhou University in Datong River Basin from March 1, 2020 to December 31, 2020. The longitude and latitude of the observation points are 102.737e, 36.692n and the altitude is 2903m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward manner. The shooting data resolution is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak End date of growing season, etc; For the coverage, firstly, the data is preprocessed, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This data set includes relative greenness index (GCC) and coverage. Due to the built-in clock error of phenological camera, the images before March 1 are taken at night and cannot be used, so the data is missing.

    2021-07-28 299 20

  • The digital elevation model of the Tibetan Plateau (2000)

    The digital elevation model of the Tibetan Plateau (2000)

    This data set is a digital elevation model of the Tibetan Plateau and can be used to assist in analysis and research of basic geographic information for the Tibetan Plateau. The raw data were the Shuttle Radar Topography Mission (SRTM) data, which were provided by Global Land Cover Network (GLCN), and the raw data were framing data , using the WGS84 coordinate system, including latitude and longitude, with a spatial resolution of 3″. After the mosaic processing, the Nodata (null data) generated in the mosaic process were interpolated and filled. After filling, the projection conversion process was performed to generate data as Albers equal area conical projection. After the conversion projection, the spatial resolution of the data was 90 m. Finally, the boundary of the Tibetan Plateau was used for cutting to obtain DEM data. This data table has two fields. Field 1: value Data type: long integer Interpretation: altitude elevation Unit: m Field 2: count Data type: long integer Interpretation: The number of map spots corresponding to the altitude elevation Data accuracy: spatial resolution: 90 m

    2021-07-19 2484 115

  • Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Minqin Station, 2020)

    Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Minqin Station, 2020)

    The data set includes phenological camera observation data of Minqin station of Lanzhou University cold and dry area scientific observation network in Shiyang River Basin from August 11, 2020 to December 31, 2020. The longitude and latitude of observation points are 103.668e, 39.208n, and the altitude is 1020m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This dataset includes relative greenness index (GCC). The phenological camera will be installed on August 11, 2020.

    2021-07-16 370 22

  • Cold and arid research network of Lanzhou university (phenology camera observation data set of Sidalong Station, 2020)

    Cold and arid research network of Lanzhou university (phenology camera observation data set of Sidalong Station, 2020)

    The data set includes phenological camera observation data of Sidalong station of Lanzhou University cold and dry area scientific observation network in Heihe River Basin from February 3, 2020 to December 31, 2020. The longitude and latitude of observation points are 99.926e, 38.428n, and the altitude is 3146m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This dataset includes relative greenness index (GCC). Photos before February 3 cannot be used due to equipment failure.

    2021-07-16 265 15

  • Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Xiyinghe Station, 2020)

    Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Xiyinghe Station, 2020)

    The data set includes the phenological camera observation data of xiyinghe station of Lanzhou University cold and dry area scientific observation network in Shiyang River Basin from January 1, 2020 to December 31, 2020. The longitude and latitude of the observation points are 101.855e, 37.561n, and the altitude is 3616m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

    2021-07-16 287 19

  • Cold and arid research network of Lanzhou university (phenology camera observation data set of Guazhou Station, 2020)

    Cold and arid research network of Lanzhou university (phenology camera observation data set of Guazhou Station, 2020)

    The data set includes phenological camera observation data of Guazhou station of Lanzhou University cold and dry area scientific observation network in Shule River Basin from March 10, 2020 to December 31, 2020. The longitude and latitude of observation points are 95.673e, 41.405n, and the altitude is 2014m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This dataset includes relative greenness index (GCC). The data before March 10 has been covered due to the memory card reaching the upper limit; Adjust camera orientation on May 31.

    2021-07-16 260 18

  • Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Suganhu Station, 2020)

    Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Suganhu Station, 2020)

    The data set includes the phenological camera observation data of suganhu station of Lanzhou University cold and dry area scientific observation network in haertang River Basin of Qaidam Basin from January 1, 2020 to December 31, 2020. The longitude and latitude of the observation points are 94.125 ° e, 38.992n, and the altitude is 2798m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

    2021-07-16 281 17

  • Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Sidalong of Qilian Mountain (2020)

    Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Sidalong of Qilian Mountain (2020)

    This dataset contains infrared camera data from January 2020 to October 2020 for the Sidalong sample area in the Qilian Mountains region of Lanzhou University. The typical habitats in the sample area of Teradalong are forests, the main tree species are Qilian round cypress and Qinghai spruce, and the typical mammals are red deer, musk deer, roe deer and blue eared-pheasant.. The main steps of infrared camera data processing include. 1. data storage, setting up directories to store photos and video files on computers, mobile hard disks or other storage media. 2. Processing of mistaken or invalid photos. Delete wind-blown, exposure, no animal presence or arbitrary form of invalid photos. 3. species identification. (1) Animal identification image library construction, each survey unit to establish a library of animal identification images, the library is mainly used for the training of species identification personnel, to facilitate their rapid grasp of species identification characteristics, accurate identification of species. (2) Processing of effective photos: for photos (videos) that can accurately identify species, fill in the name, number and environmental information of the animals in the automatic camera (video) recording form; if there are two or more animals on a photo, fill in one line each; for photos that cannot accurately identify species, fill in the column of the name of the animal that cannot be identified, and fill in the number and environmental information, and fill in the photo processing For poultry and livestock, fill in the name and number of animals and poultry and livestock; for people, fill in the name of the animal as "herder, tourist, forest ranger", etc. (3) other information: environmental information records, according to the photos (video), fill in the following environmental information: temperature: according to the temperature shown on the photos to fill in. Weather: sunny, cloudy, rain, snow. Need to judge carefully. Snow: with or without. Behavior: foraging, drinking, hunting, mating, fighting, fighting for food, repelling, playing, running, resting, walking, alerting, etc. Animal age: young, subspecies, female, male, unknown. Published observation data include: file number, file format, folder number, camera number, deployment point number, shooting date, shooting time, working days (days), element, species name, young, sub, female, male, unknown, total, behavior, temperature (℃), weather, snow.

    2021-07-15 162 0

  • Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Haxi of Qilian Mountain (2019-2020)

    Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Haxi of Qilian Mountain (2019-2020)

    This dataset contains infrared camera data from July 2019 to October 2020 for the Haxi sample area in the Qilian Mountains region of Lanzhou University. The typical habitat in the Haxi sample area is forest, the main tree species are Qilian round cypress and Qinghai spruce, and the typical mammals are red deer, musk deer, roe deer and blue eared-pheasant.. The area is heavily grazed and has frequent human activities. The main steps of infrared camera data processing include. 1. data storage, setting up directories to store photos and video files on computers, mobile hard disks or other storage media. 2. Processing of mistaken or invalid photos. Delete wind-blown, exposure, no animal presence or arbitrary form of invalid photos. 3. species identification. (1) Animal identification image library construction, each survey unit to establish a library of animal identification images, the library is mainly used for the training of species identification personnel, to facilitate their rapid grasp of species identification characteristics, accurate identification of species. (2) Processing of effective photos: for photos (videos) that can accurately identify species, fill in the name, number and environmental information of the animals in the automatic camera (video) recording form; if there are two or more animals on a photo, fill in one line each; for photos that cannot accurately identify species, fill in the column of the name of the animal that cannot be identified, and fill in the number and environmental information, and fill in the photo processing For poultry and livestock, fill in the name and number of animals and poultry and livestock; for people, fill in the name of the animal as "herder, tourist, forest ranger", etc. (3) other information: environmental information records, according to the photos (video), fill in the following environmental information: temperature: according to the temperature shown on the photos to fill in. Weather: sunny, cloudy, rain, snow. Need to judge carefully. Snow: with or without. Behavior: foraging, drinking, hunting, mating, fighting, fighting for food, repelling, playing, running, resting, walking, alerting, etc. Animal age: young, subspecies, female, male, unknown. Published observation data include: file number, file format, folder number, camera number, deployment point number, shooting date, shooting time, working days (days), element, species name, young, sub, female, male, unknown, total, behavior, temperature (℃), weather, snow.

    2021-07-15 145 0

  • Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Qifeng of Qilian Mountain (2019-2020)

    Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Qifeng of Qilian Mountain (2019-2020)

    This dataset contains infrared camera data from January 2020 to November 2020 from Qifeng sample area in Qilian Mountains region of Lanzhou University. It belongs to the Sunan Yugu Autonomous County, Zhangye City, Gansu Province, in the northwest of the Sunan Yugu Autonomous County, the western part of the Western Corridor and the northern foot of the Qilian Mountains, east of Daxiang, south of Qilian County and Tianjun County, Qinghai Province, west of Subei County, Jiuquan City, and north of Jiuquan Suzhou District, Jiayuguan City and Yumen City. The typical habitats in the Qifeng sample area are desert and alpine bare rock, and typical mammals include snow leopard, lynx, white-lipped deer and blue sheep. The main steps of infrared camera data processing include. 1. data storage, setting up directories to store photos and video files on computers, mobile hard disks or other storage media. 2. Processing of mistaken or invalid photos. Delete wind-blown, exposure, no animal presence or arbitrary form of invalid photos. 3. species identification. (1) Animal identification image library construction, each survey unit to establish a library of animal identification images, the library is mainly used for the training of species identification personnel, to facilitate their rapid grasp of species identification characteristics, accurate identification of species. (2) Processing of effective photos: for photos (videos) that can accurately identify species, fill in the name, number and environmental information of the animals in the automatic camera (video) recording form; if there are two or more animals on a photo, fill in one line each; for photos that cannot accurately identify species, fill in the column of the name of the animal that cannot be identified, and fill in the number and environmental information, and fill in the photo processing For poultry and livestock, fill in the name and number of animals and poultry and livestock; for people, fill in the name of the animal as "herder, tourist, forest ranger", etc. (3) other information: environmental information records, according to the photos (video), fill in the following environmental information: temperature: according to the temperature shown on the photos to fill in. Weather: sunny, cloudy, rain, snow. Need to judge carefully. Snow: with or without. Behavior: foraging, drinking, hunting, mating, fighting, fighting for food, repelling, playing, running, resting, walking, alerting, etc. Animal age: young, subspecies, female, male, unknown. Published observation data include: file number, file format, folder number, camera number, deployment point number, shooting date, shooting time, working days (days), element, species name, young, sub, female, male, unknown, total, behavior, temperature (℃), weather, snow.

    2021-07-14 131 0

  • Land Surface Albedo Dataset of Typical Stations in Middle Reaches of Heihe River Basin based on UAV Remote Sensing (2020, V1)

    Land Surface Albedo Dataset of Typical Stations in Middle Reaches of Heihe River Basin based on UAV Remote Sensing (2020, V1)

    Surface albedo is a critical parameter in land surface energy balance. This dataset provides the monthly land surface albedo of UAV remote sensing for typical ground stations in the middle reaches of Heihe river basin during the vegetation growth stage (June to October) in 2020 (The data of Huazhaizi station in August is not available because of technical problem). The algorithm for calculating albedo is an empirical method, which was developed based on a comprehensive forward simulation dataset based on 6S model and typical spectrums. This method can effectively transform the surface reflectance to the broadband surface albedo. The method was then applied to the surface reflectance acquired by UAV multi-spectral sensor and the broadband surface albedo with a 0.2-m spatial resolution was eventually obtained.

    2021-07-11 1415 0

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (phenology camera observation data set of Alpine meadow and grassland ecosystem Superstation, 2020)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (phenology camera observation data set of Alpine meadow and grassland ecosystem Superstation, 2020)

    This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from phenology camera observation data of the Alpine meadow and grassland ecosystem Superstation from January 1 in 2020 to December 31 in 2020. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The phenology camera adopts a vertical downward method to collect data, with the resolution of 2592*1944. Phenology photos in this data set were taken at 12:10 a day, which has a time error of ±10 min. The image is named as BSDCJZ BEIJING_IR_Year_Month_Day_Time.

    2021-07-09 485 27

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Sidaoqiao Superstation-2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Sidaoqiao Superstation-2020)

     The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream reaches of Heihe integrated observatory network before May 31 and after September 2, 2020. Due to the power failure of phenological camera, the time is lost from May 31 to September 2, 2020. In addition, after the camera was moved and reinstalled, the object in the field of view before May 31 and after September 2 would change, which may cause the inconsistency of the data before and after. The site (101.137° E, 42.001° N) was located on a tamarix (Tamarix chinensis Lour.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 873 m. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the phenology, firstly, The phenological index needs to be calculated according to the region of interest. Such as, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)). Then, controlling the quality of data, filling the invalid value and filtering smoothing are performed. Finally, the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, peak, growth season end, etc. This data set includes the relative greenness index (GCC) in 2020. Please refer to Liu et al. (2018) for sites information in the Citation section.

    2021-07-08 341 18

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Sidaoqiao superstation, 2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Sidaoqiao superstation, 2020)

    This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from July 25 to October 20 in 2020. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Sidaoqiao superstation (101.1374E, 42.0012N), which is about 30m×30m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.

    2021-07-08 395 29

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Mixed forest station, 2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Mixed forest station, 2020)

    This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from July 26 to October 20 in 2020. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Mixed forest station (101.1335E, 41.9903N), which is about 30 m×30 m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.

    2021-07-08 329 28

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Daman Superstation, 2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Daman Superstation, 2020)

    This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from June 1 to September 20 in 2020. The site (100.376° E, 38.853°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 6 observation samples, each of which is about 30m×30m in size, and the latitude and longitude are (100.376°E, 38.853°N), (100.377° E, 38.858°N), (100.374°E, 38.855°N), (100.374°E, 38.858°N), (100.371°E, 38.854°N), (100.369°E, 38.854°N). Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.

    2021-07-08 442 40

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Daman Superstation-2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Daman Superstation-2020)

    The dataset contains the phenological camera observation data of the Daman Superstation in the midstream of Heihe integrated observatory network from January 1, 2020 to December 31, 2020. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the phenology, firstly, the phenological index needs to be calculated according to the region of interest. Such as, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)). Then, controlling the quality of data, filling the invalid value and filtering smoothing are performed. Finally, the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, peak, growth season end, etc. This data set includes the relative greenness index (GCC) in 2020. Please refer to Liu et al. (2018) for sites information in the Citation section.

    2021-07-08 355 22

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of A’rou Superstation-2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of A’rou Superstation-2020)

     This dataset contains phenological camera observation data of the A’rou Superstation in the upperstream reaches of Heihe integrated observatory network from January 1 to December 31 in 2020. The site (100.372° E, 38.856° N) was located in the Daban Village, near Qilian County in Qinghai Province. The elevation is 3033 m.The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the phenology, firstly, The phenological index needs to be calculated according to the region of interest. Such as, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)). Then, controlling the quality of data, filling the invalid value and filtering smoothing are performed. Finally, the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, peak, growth season end, etc. This data set includes the relative greenness index (GCC) in 2020. Please refer to Liu et al. (2018) for sites information in the Citation section.

    2021-07-08 453 23

  • Cold and Arid Research Network of Lanzhou university (an observation system of Meteorological elements gradient of Liancheng Station, 2020)

    Cold and Arid Research Network of Lanzhou university (an observation system of Meteorological elements gradient of Liancheng Station, 2020)

    This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Liancheng Station from January 1 to November 2, 2020. The site (102.833E, 36.681N) was located on a forest in the Tulugou national forest park, which is near Liancheng city, Gansu Province. The elevation is 2912 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1.5 m), rain gauge (2 m), four-component radiometer (4 m, towards south),infrared temperature sensors (2 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation;-0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation;-0.05 and -0.1m in south of tower), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m and Ta_8 m; RH_4 m and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5 cm, Gs_10 cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential (SWP_5cm,SWP_10cm)(kpa), soil conductivity (EC_5cm,EC_10cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The format of the date and time was unified, and the date and time were collected in the same column.

    2021-07-05 314 25